Supports converting all weight/activation formats to native MulT (bf16/f32)
Also:
- ConstMat/MutableMat for const correctness
- Move RowVectorBatch to allocator.h so it can be used from Matmul
- Add matmul.h so MatMulEnv can be used from Activations
- Remove kMaxThreads, detect from PerClusterPools
- Build fix: -inl.h files must be textual_hdrs, and highway.h should precede -inl.h
```
zen4 new
64, 24576, 3072, add=0, MatTA=bf16, MatTB=sfp: 616.6 GFLOPS.
64, 3072, 24576, add=0, MatTA=bf16, MatTB=sfp: 460.7 GFLOPS.
64, 24576, 3072, add=0, MatTA=f32, MatTB=sfp: 598.6 GFLOPS.
64, 3072, 24576, add=0, MatTA=f32, MatTB=sfp: 435.6 GFLOPS.
zen4 old
64, 24576, 3072, add=0, MatTA=f32, MatTB=sfp: 257.5 GFLOPS.
64, 3072, 24576, add=0, MatTA=f32, MatTB=sfp: 231.9 GFLOPS.
```
PiperOrigin-RevId: 663729812
Split attention into functions, move into class.
Fuse Rope and MulBy, allow non-in-place version to avoid copy from q to KV.
Sink if() into MaybeLogitsSoftCap.
PiperOrigin-RevId: 661168418
Split common and weights into separate lib
Remove common-inl (does not have to be SIMD code), activations.cc
Centralize switch(Model) to avoid duplication
Move CompressWeightsT to compress_weights.cc
Move LoadWeights to weights.cc
PiperOrigin-RevId: 640869202
This is still in progress / experimental, currently it is only
implemented for normal gemma MQA attention layers, and no
parallelism is added yet for backward pass.
Since we need to remember all activations from all layers, the
forward pass was also reimplemented with a new activation data
structure.
Jan Wassenberg
Daniel Keysers
Zoltan Szabadka